Image Generation for Enterprise: Governance and Best Practices

Enterprise image generation requires governance, quality control, and clear policies.

Governance Framework

class ImageGenerationPolicy:
    def __init__(self):
        self.allowed_use_cases = [
            "marketing_materials",
            "internal_presentations",
            "prototyping",
            "training_content"
        ]

        self.prohibited_content = [
            "real_people_without_consent",
            "competitor_logos",
            "misleading_content",
            "copyrighted_characters"
        ]

        self.disclosure_required = [
            "external_marketing",
            "customer_facing",
            "social_media"
        ]

    def validate_request(self, request: dict) -> dict:
        issues = []

        if request["use_case"] not in self.allowed_use_cases:
            issues.append("Use case not approved")

        for prohibited in self.prohibited_content:
            if prohibited in request.get("prompt", "").lower():
                issues.append(f"Contains prohibited content: {prohibited}")

        needs_disclosure = request["use_case"] in self.disclosure_required

        return {
            "approved": len(issues) == 0,
            "issues": issues,
            "needs_disclosure": needs_disclosure
        }

Quality Control Pipeline

class ImageQualityPipeline:
    def __init__(self, azure_vision_client):
        self.vision = azure_vision_client

    async def validate_image(self, image_url: str) -> dict:
        """Validate generated image quality and appropriateness."""

        # Analyze with Azure Vision
        analysis = await self.vision.analyze(
            image_url,
            features=["adult", "objects", "brands"]
        )

        checks = {
            "adult_content": analysis.adult.is_adult_content == False,
            "brand_safe": len(analysis.brands.list) == 0,
            "quality_sufficient": True  # Add quality checks
        }

        return {
            "passed": all(checks.values()),
            "checks": checks,
            "analysis": analysis
        }

Metadata and Audit Trail

from dataclasses import dataclass
from datetime import datetime

@dataclass
class GeneratedImage:
    id: str
    prompt: str
    image_url: str
    generated_at: datetime
    generated_by: str
    use_case: str
    approved: bool
    disclosure_applied: bool

def log_generation(image: GeneratedImage):
    """Log image generation for audit trail."""
    # Store in audit database
    audit_db.insert(image.__dict__)

Disclosure Standards

def apply_disclosure(image_path: str, output_path: str):
    """Apply AI disclosure to generated image."""

    from PIL import Image, ImageDraw, ImageFont

    img = Image.open(image_path)

    # Add disclosure text
    draw = ImageDraw.Draw(img)
    disclosure = "AI-Generated Image"
    draw.text((10, img.height - 30), disclosure, fill=(128, 128, 128))

    img.save(output_path)

    return output_path

Best Practices

1. Define clear policies - What’s allowed, what’s not
2. Implement review - Human approval for external use
3. Track everything - Full audit trail
4. Disclose appropriately - Transparent about AI use
5. Train users - Educate on responsible use

Conclusion

Enterprise image generation success requires balancing creativity with governance. Build policies, implement controls, and maintain transparency.